Understanding the potential of generative AI (GenAI)-based attacks on the power grid is a fundamental challenge that must be addressed in order to protect the power grid by realizing and validating risk in new attack vectors. In this paper, a novel zero trust framework for a power grid supply chain (PGSC) is proposed. This framework facilitates early detection of potential GenAI-driven attack vectors (e.g., replay and protocol-type attacks), assessment of tail risk-based stability measures, and mitigation of such threats. First, a new zero trust system model of PGSC is designed and formulated as a zero-trust problem that seeks to guarantee for a stable PGSC by realizing and defending against GenAI-driven cyber attacks. Second, in which a domain-specific generative adversarial networks (GAN)-based attack generation mechanism is developed to create a new vulnerability cyberspace for further understanding that threat. Third, tail-based risk realization metrics are developed and implemented for quantifying the extreme risk of a potential attack while leveraging a trust measurement approach for continuous validation. Fourth, an ensemble learning-based bootstrap aggregation scheme is devised to detect the attacks that are generating synthetic identities with convincing user and distributed energy resources device profiles. Experimental results show the efficacy of the proposed zero trust framework that achieves an accuracy of 95.7% on attack vector generation, a risk measure of 9.61% for a 95% stable PGSC, and a 99% confidence in defense against GenAI-driven attack.

翻译：理解基于生成式人工智能（GenAI）对电网的攻击潜力是一项根本性挑战，必须通过识别和验证新攻击向量中的风险来保护电网。本文提出了一种针对电网供应链（PGSC）的新型零信任框架。该框架能够早期检测潜在的GenAI驱动攻击向量（例如重放攻击和协议型攻击），评估基于尾部风险的稳定性指标，并缓解此类威胁。首先，设计并形式化了PGSC的新零信任系统模型，将其表述为一个通过识别和防御GenAI驱动的网络攻击来保证电网稳定性的零信任问题。其次，开发了一种特定领域的基于生成对抗网络（GAN）的攻击生成机制，以构建新的脆弱性网络空间，从而进一步理解此类威胁。第三，建立并实施了基于尾部风险的风险量化指标，用于量化潜在攻击的极端风险，同时利用信任评估方法进行持续验证。第四，设计了一种基于集成学习的自助聚合方案，用于检测生成具有令人信服的用户及分布式能源资源设备配置文件合成身份的攻击。实验结果表明，所提出的零信任框架在攻击向量生成上达到95.7%的准确率，在95%稳定PGSC中风险指标为9.61%，且针对GenAI驱动攻击的防御置信度为99%。